Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

Płotka Szymon, Włodarczyk Tomasz, Szczerba Ryszard, Chomiak Anna, Komisarek Oskar. Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review. Journal of Education, Health and Sport. 2021;11(01):94-104. eISSN 2391-8306. DOI http://dx.doi.org/10.12775/JEHS.2021.11.01.010

https://apcz.umk.pl/czasopisma/index.php/JEHS/article/view/JEHS.2021.11.01.010

https://zenodo.org/record/4459147

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Received: 25.12.2020. Revised: 30.12.2020. Accepted: 17.01.2021.

Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

Szymon Płotka[1], Tomasz Włodarczyk [1], Ryszard Szczerba [1], Anna Chomiak [2], Oskar Komisarek [2]

1 Institute of Informatics, Warsaw University of Technology, Warsaw, Poland

2 Department of Maxillofacial Orthopaedics and Orthodontics, Poznan University of Medical Sciences, ul. Bukowska 70, 60–812 Poznań, Poland

Abstract

Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. In this work, we describe the current methods, the architectures of deep convolutional neural networks used in CBCT. Literature from 2000-2020 from the PubMed database, Google Scholar, was analyzed. Account has been taken of publications in English that describe architectures of deep convolutional neural networks used in CBCT. The results of the reviewed studies indicate that deep learning methods employed in orthodontics can be far superior in comparison to other high-performing algorithms.

Key words: CBCT; Cone-Beam Computed Tomography; deep learning; machine learning; orthodontics